Datadog vs. Logstash

A one-stop solution for everything you need. Multiple functionalities are tailored to meet specific business needs. Logs are essential for any business, and Datadog manages logs effectively. Rum sessions are something new to me and have given us a new perspective on how to reverse engineer issues that we see for our customers.

Incentivized

Logstash is a must in an ELK stack, which I am sure is going to be the #1 case. At any point when you have several sources, Logstash can be the common point to aggregate, and categorize those data. Then send this new data to its destination. Very handy. It is free and open source. It may not be appropriate to analyze data-sets dependent on each other but from a different data source. Reason being Logstash works on data at hand, and not wait for other data to arrive. It would be unwise for Logstashh to handle complicated, long-running transformations because this is injected and ejected. The faster you do it, the safer.

Incentivized

• Create Dashboards as per application, environments, and Custom metrics in one panel.
• Log aggregation, one-stop Application monitoring tools for the whole infrastructure.
• Playbooks, SLA definition, success and error quotas, request visualizations.
• DB monitoring, Serverless stack monitoring.
• Alerting of Production incidents so we can quickly resolve the issues on time.

Incentivized

• Plugin ecosystem allows modular extensions.
• Tight integration into the Elastic.com products of Beats and Elasticsearch, so minimal setup is required when using those tools.
• Filter plugins are powerful for extracting and enriching input data.

Incentivized

• Alert windows cause lag in notifications (e.g. if the alert window is X errors in 1 hour, we won't get alerted until the end of the 1 hour range)
• I would appreciate more supportive examples for how to filter and view metrics in the explorer
• I would like a more clear interface for metrics that are missing in a time frame, rather than only showing tags/etc. for metrics that were collected within the currently viewed time frame

Incentivized

• Memory: Logstash is a HOG, if you are deploying it on commodity (i.e. cheap and old) hardware: You will need at least 2GB, just for Logstash. So don't expect to run your entire ELK stack on one AMD Athlon machine.
• Overlap: Logstash fills in an area of the ELK stack that makes the most sense: as a log file transformer / shipper. However, if you start breaking that stack, with the addition of other components- you start seeing where features of Logstash may be implemented or solved in the additional components much easier (or better, or to a higher degree of resolution)
• More Overlap: Since my team employs Syslog-ng extensively- Logstash can sometimes get in the way (and this may be a problem for DevOps stacks overall): You can configure Syslog to record certain information from a source, filter that data, and even export that data in a particular format. Logstash will pick that data up, and then parse it. However, if you don't keep your Syslog-ng configuration files, and your Logstash configuration files in sync, your results will not be what you expected, and this will translate into (sometimes) hours/days of work, hunting down a line item in a configuration file.

Incentivized

Datadog's user interface is quite friendly and easy to navigate. With menus clearly categorized, and ability to bookmark important dashboards, one can easily find what they're looking for. For dashboards, ability to move and resize visualizations and group them, is really helpful to organize dashboards. Automatic suggestions from Datadog for important visualizations based on the metrics and logs would provide another level of ease of use.

Incentivized

As I said earlier, for a production-grade OpenStack Telco cloud, Logstash brings high value in flexibility, compliance, and troubleshooting efficiency. However, this brings a higher infra & ops cost on resources, but that is not a problem in big datacenters because there is no resource crunch in terms of servers or CPU/RAM

The support team usually gets it right. We did have a rather complicate issue setting up monitoring on a domain controller. However, they are usually responsive and helpful over chat. The downside would be I don’t think they have any phone support. If that is important to you this might not be a good fit.

Incentivized

I selected Datadog because of its features and the wide range of integration support. As I already told it supports more that 600+ integrations which helps and organization to keep everything in a single place and also its AI feature which is reducing the time for root cause analysis. Its custom dashboards features which helps us to visualize the data in a more attractive way.

Incentivized

MongoDB and Azure SQL Database are just that: Databases, and they allow you to pipe data into a database, which means that alot of the log filtering becomes a simple exercise of querying information from a DBMS. However, LogStash was chosen for it's ease of integration into our choice of using ELK Elasticsearch is an obvious inclusion: Using Logstash with it's native DevOps stack its really rational

Incentivized

• Saved us (time & money) from developing our own monitoring utilities that would pale in comparison
• Alerts allow us to remedy issues before our customers even know about them
• Tracking resource usage over time allows us to better plan for future needs, before it becomes a pain-point.

Incentivized

• It is very difficult to give any figures on ROI, as it depends on many factors, and in a Telcocloud environment, it is much complex to find out; however, I would give some points below on ROI
• ROI based on flexibility is very high, as it reduces the time to find RCA
• ROI based on integration is very high because it supports multi-vendor environments, avoiding vendor lock-in & works across multi-cloud setups
• ROI on resource consumption is less because Logstash in 2-3 times more resource-intensive as compared to its lightweight alternatives resulting in latency